Results 31 to 40 of about 762,527 (191)
BioData Mining, 2021 Background Missing data is a common issue in different fields, such as electronics, image processing, medical records and genomics. They can limit or even bias the posterior analysis.
Ben Omega Petrazzini +4 more
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Analysis of Pregnancy and Other Factors on Detection of Human Papilloma Virus (HPV) Infection Using Weighted Estimating Equations for Follow-Up Data [PDF]
, 2000 Generalised estimating equations have been well established to draw inference for the marginal mean from follow-up data. Many studies suffer from missing data that may result in biased parameter estimates if the data are not missing completely at random.
Chang-Claude, J. +2 more
core +2 more sources
Problems in dealing with missing data and informative censoring in clinical trials
Current Controlled Trials in Cardiovascular Medicine, 2002 A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to ...
Shih Weichung
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Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study
BMC Medical Research Methodology, 2020 Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative.
Matthew Sperrin, Glen P. Martin
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Missing at random, likelihood ignorability and model completeness
The Annals of Statistics, 2004 This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a model for the missing data mechanism (MDM).
Lu, Guobing, Copas, John B.
openaire +3 more sources
Ensaio, 2020 Almost all quantitative studies in educational assessment, evaluation and educational research are based on incomplete data sets, which have been a problem for years without a single solution.
Maria Eugénia Ferrão +2 more
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On Non-Random Missing Labels in Semi-Supervised Learning [PDF]
International Conference on Learning Representations, 2022 Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in which the label Missing Not At Random (MNAR) problem is more realistic and challenging, compared to the widely-adopted yet naive Missing Completely At Random assumption where ...
Xinting Hu +4 more
semanticscholar +1 more source
Generating Synthetic Missing Data: A Review by Missing Mechanism
IEEE Access, 2019 The performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate configuration) or in several features (multivariate configuration) at different ...
Miriam Seoane Santos +5 more
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Health and Quality of Life Outcomes, 2010 Background Missing items are common in quality of life (QoL) questionnaires and present a challenge for research in this field. The development of sound strategies of replacement and prevention requires accurate knowledge of their type and determinants ...
Coste Joël, Peyre Hugo, Leplège Alain
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Turkish Journal of Agriculture: Food Science and Technology, 2015 The purpose of this study was to evaluate the performance of multiple imputation method in case that missing observation structure is at random and completely at random from the approach of general linear mixed model.
Gazel Ser, Cafer Tayyar Bati
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